Ieee-Caa Journal of Automatica Sinica最新文献

{"title":"Parallel Theaters in CPSS: From Shadows of ISDOS to Intelligence of Decision Theaters","authors":"Qinghua Ni;Buday Viktória;Fei Lin;Jun Huang;Levente Kovács;Nan Zheng;Fei-Yue Wang","doi":"10.1109/JAS.2025.125567","DOIUrl":"https://doi.org/10.1109/JAS.2025.125567","url":null,"abstract":"As Artificial Intelligence (AI) is moving fast from Large Language Models (LLMs) to AI Agents and Agentic Intelligence, the need to incorporate new AI into Decision Intelligence (DI) is becoming more and more urgent for both practical and theoretic reasons: both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents, and both traditional and recent thinking must be rethought and reconstructed accordingly. Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering (CASE) and recent efforts in digital theatrical technology [1].","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1059-1062"},"PeriodicalIF":15.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving","authors":"Long Chen;Peng Deng;Lingxi Li;Xuemin Hu","doi":"10.1109/JAS.2025.125201","DOIUrl":"https://doi.org/10.1109/JAS.2025.125201","url":null,"abstract":"Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1272-1282"},"PeriodicalIF":15.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems","authors":"Haoran Tan;Xueming Zhang;Yaonan Wang;You Wu;Yun Feng;Zhongsheng Hou","doi":"10.1109/JAS.2024.124938","DOIUrl":"https://doi.org/10.1109/JAS.2024.124938","url":null,"abstract":"In this paper, a novel data-driven bipartite consensus control scheme is proposed for the rotation problem of large workpieces with multi-robot systems (MRSs) under a directed communication topology. The rotation of a large workpiece is described as the MRSs with cooperation and antagonism interaction. By the signed graph theory, it is further transformed into a bipartite consensus control problem, where all followers are uniformly degenerated into the general nonlinear systems based on the lateral error model. To augment the flexibility of control protocol and improve control performance, a higher-dimensional full form dynamic linearization (FFDL) technique is committed to the MRSs. The control input criterion function consists of the data model based on FFDL and the bipartite consensus error based on the signed graph theory, and the proposed control protocol is given by optimizing this criterion function. In this way, this scheme has a higher degree of freedom and better adaptive adjustment capability while not excessively increasing the control method complexity, and it can also be compatible with other forms of dynamic linearization techniques in MRSs. Further, three matrix norm lemmas are introduced to deal with the challenges of stability analysis caused by higher matrix dimensions and more robots. Finally, the effectiveness of the proposed method is verified by numerical simulations.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1144-1158"},"PeriodicalIF":15.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Switching in Sliding Mode Control: A Spatio-Temporal Perspective","authors":"Xinghuo Yu","doi":"10.1109/JAS.2025.125423","DOIUrl":"https://doi.org/10.1109/JAS.2025.125423","url":null,"abstract":"Sliding mode control (SMC) is a widely adopted control technology known for its robustness and simplicity. The essence of SMC is to use discontinuous control to drive a system into a pre-defined motion, called the sliding mode, which is designed with desirable dynamical properties. In the sliding mode, the controlled system is insensitive to the matched uncertainties and disturbances. Most SMC theory and methods have been developed based on the dynamical systems in the continuous-time domain, where switching functions play a critical role. Ideal switching is supposed to be instantaneous, activating as soon as the switching condition is met. However, in practice, switching mechanisms are affected by imperfections such as time delays, unmodeled dynamics, defects, digitization effects, and actuation limitations, which can degrade the salient properties of SMC. Understanding these effects and developing mitigation strategies are essential for industrial applications. Furthermore, the advent of networked control environments presents new challenges like limited communication bandwidth, latency and cyberattack, which have seen the emergence of the event-triggered SMC recently. Despite these significant advances, there is a lack of comprehensive studies which examine the commonalities and distinctions of utilizing switching in SMC across the continuous-time and discrete-time domains and beyond. This paper investigates the role of switching in SMC from a spatio-temporal perspective, considering both state-space and time aspects. The aim is to facilitate better understanding of its effects and misbehaviors, and to unlock its full potential for future applications. The interplay between SMC methods in the continuous-time and discrete-time domains is analyzed, and their shared principles and unique challenges are identified. Furthermore, important technical issues relating to switching across these time domains are explored, and several myths and pitfalls in their theory and applications are depicted. The relationships of SMC with other switching-based control systems such as switched control systems, fuzzy control systems, and event-triggered control systems are discussed. The impact of networked control environments on SMC in the continuous-time and discrete-time domains is also examined. Finally, key challenges and opportunities are outlined for future work in SMC and beyond.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1063-1071"},"PeriodicalIF":15.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Proportional Integral Controller-Enhanced Non-Negative Latent Factor Analysis Model","authors":"Ye Yuan;Siyang Lu;Xin Luo","doi":"10.1109/JAS.2024.125055","DOIUrl":"https://doi.org/10.1109/JAS.2024.125055","url":null,"abstract":"A non-negative latent factor (NLF) model is able to be built efficiently via a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm for performing precise representation to high-dimensional and incomplete (HDI) matrix from many kinds of big-data-related applications. However, an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information, making a resultant model suffer from slow convergence. To address this issue, this study proposes a proportional integral (PI) controller-enhanced NLF (PI-NLF) model with two-fold ideas: 1) Designing an increment refinement (IR) mechanism, which formulates the current and past update increments as the proportional and integral terms of a PI controller, thereby assimilating the past update information into the learning scheme smoothly with high efficiency; 2) Deriving an IR-based SLF-NMU (ISN) algorithm, which updates a latent factor following the principle of an IR mechanism, thus significantly accelerating an NLF model's convergence rate. The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix. The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system, social network, and cloud service system. The code is available at https://github.com/yuanyeswu/PINLF/blob/mainIPINLF-code.zip.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1246-1259"},"PeriodicalIF":15.3,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Distributed Algorithms for Aggregative Games","authors":"Huaqing Li;Jun Li;Liang Ran;Lifeng Zheng;Tingwen Huang","doi":"10.1109/JAS.2024.124998","DOIUrl":"https://doi.org/10.1109/JAS.2024.124998","url":null,"abstract":"Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 5","pages":"859-871"},"PeriodicalIF":15.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transactional-Behavior-Based Hierarchical Gated Network for Credit Card Fraud Detection","authors":"Yu Xie;MengChu Zhou;Guanjun Liu;Lifei Wei;Honghao Zhu;Pasquale De Meo","doi":"10.1109/JAS.2025.125243","DOIUrl":"https://doi.org/10.1109/JAS.2025.125243","url":null,"abstract":"The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1489-1503"},"PeriodicalIF":15.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization Algorithms Based on Double-Integral Coevolutionary Neurodynamics in Deep Learning","authors":"Dan Su;Jie Han;Chunhua Yang;Weihua Gui","doi":"10.1109/JAS.2025.125210","DOIUrl":"https://doi.org/10.1109/JAS.2025.125210","url":null,"abstract":"Deep neural networks are increasingly exposed to attack threats, and at the same time, the need for privacy protection is growing. As a result, the challenge of developing neural networks that are both robust and capable of strong generalization while maintaining privacy becomes pressing. Training neural networks under privacy constraints is one way to minimize privacy leakage, and one way to do this is to add noise to the data or model. However, noise may cause gradient directions to deviate from the optimal trajectory during training, leading to unstable parameter updates, slow convergence, and reduced model generalization capability. To overcome these challenges, we propose an optimization algorithm based on double-integral coevolutionary neurodynamics (DICND), designed to accelerate convergence and improve generalization in noisy conditions. Theoretical analysis proves the global convergence of the DICND algorithm and demonstrates its ability to converge to near-global minima efficiently under noisy conditions. Numerical simulations and image classification experiments further confirm the DICND algorithm's significant advantages in enhancing generalization performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 6","pages":"1236-1245"},"PeriodicalIF":15.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Event-Triggered Nash Equilibrium Seeking for Aggregative Game with Second-Order Dynamics","authors":"Yi Huang;Jian Sun;Qing Fei","doi":"10.1109/JAS.2024.124830","DOIUrl":"https://doi.org/10.1109/JAS.2024.124830","url":null,"abstract":"Dear Editor, This letter studies the distributed Nash equilibrium seeking problem of aggregative game, in which the decision of each player obeys second-order dynamics and is constrained by nonidentical convex sets. To seek the generalized Nash equilibrium (GNE), a projection-based distributed algorithm via constant step-sizes is developed with linear convergence. In particular, a variable tracking technique is incorporated to estimate the aggregative function, and an event-triggered mechanism is designed to reduce the communication cost. Finally, a numerical example demonstrates the theoretical results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1519-1521"},"PeriodicalIF":15.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Type Feature Fusion Network Based on Importance Weighting for Occluded Human Pose Estimation","authors":"Jiahong Jiang;Nan Xia;Siyao Zhou","doi":"10.1109/JAS.2024.124953","DOIUrl":"https://doi.org/10.1109/JAS.2024.124953","url":null,"abstract":"Human pose estimation is a challenging task in computer vision. Most algorithms perform well in regular scenes, but lack good performance in occlusion scenarios. Therefore, we propose a multi-type feature fusion network based on importance weighting, which consists of three modules. In the first module, we propose a multi-resolution backbone with two feature enhancement sub-modules, which can extract features from different scales and enhance the feature expression ability. In the second module, we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology. In the third module, we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes, thereby improving the feature extraction ability. We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017 (COCO2017), COCO-Whole-body and CrowdPose, achieving the accuracy of 78.9%, 67.1% and 77.6%, respectively. Additionally, a series of ablation experiments are designed to show the performance of our work. Finally, we present the visualization of different scenarios to verify the effectiveness of our work.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"789-805"},"PeriodicalIF":15.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}